1. Field of the Invention
The present invention relates generally to the field of medical imaging, and, more particularly, to introducing a shape index into weighted voting schemes for detecting objects in an image.
2. Description of the Related Art
Lung cancer is one of the most lethal kinds of cancer worldwide. Its cure depends critically on disease detection in the early stages. Computed tomography (“CT”) technology is an important tool for detecting and diagnosing pulmonary nodules. Due to the large amount of image data generally created by thoracic CT examination, interpreting lung CT images to detect nodules can be a very challenging task for radiologists and other health professionals. Computer-aided diagnosis (“CAD”) is a widely-accepted tool for aiding radiologists in interpreting lung nodule CT images.
A number of techniques for detecting nodules have been developed based on chest radiographs or CT images. A first approach obtains nodule candidates using multiple gray-level thresholding methods. Then a rule-based approach using two-dimensional (“2D”) geometric features assigns a confidence rating to each suspected area. A second approach introduces sphericity as a three dimensional (“3D”) feature, and includes additional gray-level features to a linear discriminant analysis (“LDA”) classifier. A third approach uses a fuzzy clustering method to segment the entire lung into two parts: a) air part, and b) blood vessels and nodules. Then, a rule-based method classifies the nodule candidates. Neural network (“NN”) methods have also been developed for detecting nodules. For example, to localize nodules in chest radiographs, a fourth approach establishes 2 NNs, with the first one detecting the suspected areas, and the second one acting as a classifier. In a fifth approach, which uses template-based methods for detecting CT nodule, Gaussian distribution of gray-level simulate nodules. Simulated sphere-like nodule templates detect nodules in the lung area using 3D processing techniques. Half-circle templates detect lung wall nodules based on 2D operations.